基本解释：M 估计
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Strong Consistency of M Estimator in Linear Model;本文研究线性模型中回归参数 M 估计的强相合性,与专著[1]中相应结论比较,我们给出了一个较弱的充分条件,将有界性条件推广到无界情形。
Although M-estimation as the object function can be used to solve the problem,its corresponding influence function is determined by the absolute value of gross error and it is a key problem to choose initial parameters.虽然以M-估计作为目标函数可以解决这个问题,但由于其对应的影响函数由残差绝对值决定,因此如何选择初始参数值成为一个关键问题。
This method is based on M-estimation.针对存在粗差或异常数据点时,最小二乘定位方法会产生定位错误的情况,本文提出了基于M-估计的稳健标靶球定位方法。
An Algorithm of Multi-Sensors Weighted Fusion Based on M-Estimate Applied in Single-Point Positioning;基于M估计的多传感器加权融合法在单点定位中的应用
Multi-Sensor Data Robust Weighted Fusion Algorithm Simulation Based on M-estimate;基于M估计的多传感器数据稳健加权融合算法仿真
M-estimate Based Kalman Filter with Immunity to Outliers;基于M估计的抗野值卡尔曼滤波方法
Research of Adaptively H_∞ Filter Based on M-estimation;基于M估计的H_∞自适应滤波技术研究
Parameter Identification Based on M-estimation and Iterative Algorithm in the Case of Non-Gaussian Noise;非高斯噪声下系统参数M估计及其递推算法
The weak consistences of M-estimation on partial linear models are studied in this paper.为得到部分线性模型中未知函数和未知系数的稳健估计,讨论了部分线性模型的M估计,用局部线性方法给出常系数的初估计,再用平均方法给出常系数的M估计,用两步方法给出函数系数的M估计,并进一步证明了未知函数和参数估计的弱一致性。
The M-estimate of the local linear regression with variable bandwiths;变窗宽局部线性回归中的M-估计
A comparative study on M-estimate in the robust statistics and the anisotropic diffusion equation proposed by Perona and Malik is given in the paper.通过比较研究Perona和Malik提出的各向异性扩散方程与稳健统计学中的M-估计,从理论上揭示了各向异性扩散的数学本质,可视为M-估计在图像处理领域的一种典型应用。
